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A comment on “A Systematic Review and Meta-analysis of the Evidence on Learning During the COVID-19 Pandemicâ€

Author

Listed:
  • Nino Buliskeria

    (Department of Economics, Nazarbayev University)

  • Ali Elminejad

    (Department of Economics, Nazarbayev University)

  • Tomas Havranek

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University)

  • Zuzana Irsova

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University)

  • Stepan Jurajda

    (CERGE-EI, a joint workplace of the Center for Economic Research and Graduate Education of Charles University and the Economics Institute of the Czech Academy of Sciences)

  • Marek Kapicka

    (CERGE-EI, a joint workplace of the Center for Economic Research and Graduate Education of Charles University and the Economics Institute of the Czech Academy of Sciences)

  • Martina Luskova

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University)

Abstract

Betthäuser et al. (2023) examine the effects of the COVID-19 pandemic on the learning progress of school-aged children. They collect 291 estimates from 42 studies. Their meta-analysis-corrected estimate implies a substantial decline in students’ learning (Cohen’s d = −0.14, 95% confidence interval −0.17 to −0.10). First, we successfully reproduce the main results and the majority of supporting figures. Second, we provide additional analysis addressing publication bias by implementing correction techniques: PET-PEESE (funnelbased), 3PSM (selection model), and RoBMA (model averaging). Additionally, we implement novel approaches that account for the strength of biased selection favoring affirmative results in the sample of analyzed studies. Third, we use techniques that assume the presence of p-hacking (MAIVE, RTMA). Using these methods, the corrected effect ranges from −0.25 to −0.11 with high statistical significance. While our analysis does reveal some evidence of publication bias and p-hacking, these phenomena do not appear to systematically distort the overall findings of the original study.

Suggested Citation

  • Nino Buliskeria & Ali Elminejad & Tomas Havranek & Zuzana Irsova & Stepan Jurajda & Marek Kapicka & Martina Luskova, 2024. "A comment on “A Systematic Review and Meta-analysis of the Evidence on Learning During the COVID-19 Pandemicâ€," Working Papers IES 2024/41, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised 2024.
  • Handle: RePEc:fau:wpaper:wp2024_41
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    File URL: https://ies.fsv.cuni.cz/en/comment-systematic-review-and-meta-analysis-evidence-learning-during-covid-19-pandemic
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    More about this item

    Keywords

    Replication; Robustness; Meta-analysis; COVID-19; Education; Learning deficit;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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